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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bae9b48b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, LogisticRegression \n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, \\\n",
    "                             GradientBoostingClassifier, GradientBoostingRegressor\n",
    "from sklearn.svm import SVC, SVR\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "12a97393",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9733333333333334"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_scaler = StandardScaler()\n",
    "X, y = datasets.load_iris(return_X_y=True)\n",
    "X = std_scaler.fit_transform(X, y)\n",
    "\n",
    "\n",
    "logistic_regressor = LogisticRegression().fit(X, y)\n",
    "logistic_regressor.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cab53af7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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